polymarket-paper-trader

A zero-risk gym for AI agents on real prediction markets — real order books, official fees, verified fill fidelity. Practice, evaluate, and benchmark decision intelligence.

Agents make probability judgments all day. Polymarket is the world's largest prediction market, and its order books are the honest scoreboard: real money, real prices, real outcomes. But you cannot hand an agent a wallet to learn with. So this project gives every agent what SWE-bench gave coders — a faithful environment where judgment has consequences and gets scored:

• Practice — your agent trades $10k of paper money against live Polymarket order books, with the same fee model and fill mechanics as the real exchange
• Evaluate — the polymarket-benchmark harness in this repository (installed separately) scores any model on prediction-market decision sets (Brier score, calibration, alpha)
• Compare — multi-account battles and leaderboards rank agents against each other

Part of agent-next — building an agentic world.

60-second demo

npx clawhub install polymarket-paper-trader    # install via ClawHub
pm-trader init --balance 10000                 # $10k paper money
pm-trader markets search "bitcoin"             # find markets
pm-trader buy will-bitcoin-hit-100k yes 500    # buy $500 of YES
pm-trader stats --card                         # shareable stats card

That's it. Your AI agent is now trading Polymarket with zero risk.

Install

# via pip
pip install polymarket-paper-trader

# via ClawHub (for OpenClaw agents)
npx clawhub install polymarket-paper-trader

# from source (development)
uv pip install -e ".[dev]"

Requires Python 3.10+.

Quick start

# Initialize with $10k paper balance
pm-trader init --balance 10000

# Browse markets
pm-trader markets list --sort liquidity
pm-trader markets search "bitcoin"

# Trade
pm-trader buy will-bitcoin-hit-100k yes 100      # buy $100 of YES
pm-trader sell will-bitcoin-hit-100k yes 50       # sell 50 shares

# Check portfolio and P&L
pm-trader portfolio
pm-trader stats

How it works — and why to trust it

• Your order walks the real book. A buy consumes live ask levels from the lowest price upward, exactly like a real taker order — slippage is real and reported in basis points
• Fees follow the official per-match curve — fee = C × rate × p × (1-p), rounded to 5 decimals, makers exempt (exact spec in the CHANGELOG) — charged per filled level from each market's published fee schedule, not an approximation
• Paper cash, real discipline. Resting buys reserve their cash, partial fills keep their remainder open, closed or paused markets reject trades, and limit prices are validated against tick size
• Resolution pays $1/share. Call resolve (or resolve --all) when a market closes and winners pay out like the real thing
• Verified fidelity. The live test suite asserts that simulated fills land inside the band of prices the market actually quoted (Data API v2 price history) and that fees match the official curve exactly — run against real APIs on CI
• Upstream-aligned. The client tracks the current Gamma / CLOB / Data API surface, contract-verified by live probes
• Multi-outcome markets — any number of outcomes, not just YES/NO
• 100% coverage gate on the core package, plus end-to-end tests against the live API

CLI commands

Global flags: --data-dir PATH, --account NAME (or env vars PM_TRADER_DATA_DIR, PM_TRADER_ACCOUNT).

MCP server — what your agent can do

Your agent gets the following tools via the Model Context Protocol:

pm-trader-mcp  # starts on stdio

Add to your Claude Code config:

{
  "mcpServers": {
    "polymarket-paper-trader": {
      "command": "pm-trader-mcp"
    }
  }
}

MCP tools

Strategy examples

Three ready-to-use strategies in examples/:

Momentum (examples/momentum.py)

Buys when YES price crosses above 0.55, takes profit at 0.70, stops loss at 0.35.

pm-trader strategy run examples.momentum.run

Mean reversion (examples/mean_reversion.py)

Buys when YES price drops 12+ cents below 0.50 fair value, sells when it reverts.

pm-trader strategy run examples.mean_reversion.run

Limit grid (examples/limit_grid.py)

Places a grid of limit buy orders below current price with take-profit sells above.

pm-trader strategy run examples.limit_grid.run

Writing your own strategy

Strategies are imported from the examples. package (the allowlist lives in pm_trader/benchmark.py), so drop your file there:

# examples/my_strategy.py
from pm_trader.engine import Engine

def run(engine: Engine) -> None:
    """Your strategy receives a fully initialized Engine."""
    markets = engine.api.search_markets("crypto")
    for market in markets:
        if market.closed or market.yes_price < 0.3:
            continue
        engine.buy(market.slug, "yes", 100.0)

pm-trader strategy run examples.my_strategy.run

For backtesting with historical data:

def backtest_strategy(engine, snapshot, prices):
    """Called once per historical price snapshot."""
    if snapshot.midpoint > 0.6:
        engine.buy(snapshot.market_slug, snapshot.outcome, 50.0)

Evaluate your agent: polymarket-benchmark

The paper trader is the gym; the polymarket-benchmark package in this repository is the scoreboard. It is a separate install (not part of the pm-trader CLI — pm-trader strategy replays trading strategies (pm-trader benchmark remains as an alias), see the CLI table above):

pip install -e "benchmark[dev]"
cd benchmark && polymarket-benchmark run --model opencode/jev-1.13-free --market-set mini

Run two models head-to-head to see whose judgment is actually better. Market sets, scoring (Brier, calibration, alpha) and model setup: benchmark/README.md.

Multi-account support

Run parallel strategies with isolated accounts:

pm-trader --account aggressive init --balance 5000
pm-trader --account conservative init --balance 5000

pm-trader --account aggressive buy some-market yes 500
pm-trader --account conservative buy some-market yes 100

pm-trader strategy compare aggressive conservative

Share your results

Generate a shareable stats card and post to X/Twitter:

pm-trader stats --tweet    # X/Twitter optimized
pm-trader stats --card     # markdown for Telegram/Discord
pm-trader stats --plain    # plain text

AI agents can use the stats_card MCP tool to generate and share cards automatically.

Honest limits

• Paper only. No wallet, no keys, no real trades, no real money — ever. Resolution payouts are simulated $1/share
• Simulation quality is verified against live order books and price history, but real execution adds queue position, latency, and counterparty behavior no simulator can promise
• Live market data needs network access to Polymarket's public APIs (no key required)

OpenClaw / ClawHub

Available on ClawHub as polymarket-paper-trader:

npx clawhub install polymarket-paper-trader

GitHub bot

Comment /oc or /opencode on an issue or PR. New issues get a triage reply; non-draft PRs get a shallow review. The public bot uses FreeInference (qwen3.6-35b) via a repo Actions secret — no wallet, no real trades. Sessions are not shared.

Also in this repository

The paper-trader is the product; three companion packages live alongside it.

See CONTRIBUTING.md for how to work on each package and CHANGELOG.md for release history.

Tests

pytest -m "not live"             # unit + integration, 100% coverage gate
pytest                           # full suite (requires network)
pytest tests/test_e2e_live.py    # live API integration tests only

License

MIT